Alzheimer's Disease

Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.

Early identification of patients with Alzheimer's disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-stratified follow-up. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI), we developed two prognostic models for 12-month Mini-Mental State Examination (MMSE) decrease (≥ 3 points): (i) a clinical logistic-regression model and (ii) a random-...

Identifying serum amino acid as biomarkers of gestational diabetes mellitus in second-trimester among Chinese pregnant women: a machine learning-based cross-sectional study.

BACKGROUND: Levels of plasma branched-chain and aromatic amino acids in pregnancy have been associated with gestational diabetes mellitus (GDM), but the metabolic role of serum amino acid (AA) profiles in its pathogenesis remains insufficiently elucidated. OBJECTIVE: This study evaluated the diagnostic potential of second-trimester serum AA profiles, including Cys, Met, Val, Lys, Cit, Tau, Asp, Il...

Improved polygenic risk prediction for alzheimer's disease and related dementias using deep learning: age and APOE-stratified analysis.

Early detection of Alzheimer's disease based on leveraging multimodal features of the clock drawing test.

BackgroundAlthough multi-task handwriting analysis has the potential to improve early detection of Alzheimer's disease (AD), the educational bias inhe...

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and ma...

Exploratory analysis of the associations of the brain age gap with cognitive function and amyloid-β accumulation: participants selection based on metabolic and physiological blood markers.

The brain age gap (BAG) is defined as the difference between brain age estimated from MRI using artificial intelligence and chronological age, and has...

Amyloid PET quantification with deep learning segmentation models without MRI.

Establishing a relationship between iron-based blood measures and structural brain changes using neural networks in UK Biobank.

BACKGROUND: Disturbance of iron homeostasis in both the brain and blood is linked to cognitive impairment and neurodegenerative diseases. Investigatio...

Dual modal data-driven virtual reality-based mild cognitive Impairment assessment using MCIformer.

BACKGROUND: Mild Cognitive Impairment (MCI) assessment is critical for identifying cognitive decline and enabling early intervention to reduce the ris...

HIF3A-mediated aberrant activation of TXNIP promotes Alzheimer's disease progression.

Oxidative stress (OS) is a hallmark of Alzheimer's disease (AD), yet the cell type-specific mechanisms remain unclear. We analyzed a single-cell RNA s...

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study,...

Fusing serum peptidome profiles with clinical variables via machine learning for pulmonary embolism risk assessment.

Pulmonary embolism (PE) remains a life-threatening cardiovascular emergency that requires timely and accurate diagnosis. Current diagnostic strategies...

Correction to "Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay".

EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. Electroencephalography (EEG), a non-invasive, cost-ef...

Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.

Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.

Artificial intelligence (AI) is transforming biomarker discovery in neurology by overcoming key limitations of conventional approaches that are often ...

2.5D HAU-Net with gated spatial attention for automatic hippocampus segmentation in MRI.

BACKGROUND: The hippocampus is a key brain region and biomarker for Alzheimer's disease (AD). Accurate automated hippocampal segmentation is essential...

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment ...

Machine Learning-Integrated Pharmacophore, DFT Analysis, and molecular dynamics of Diosmetin as a potent ache inhibitor with neuroprotective activity in a Scopolamine-Induced alzheimer's zebrafish model.

Alzheimer's Disease (AD) is a degenerative disorder of the brain that causes a gradual loss of cognitive function. The cholinergic hypothesis suggests...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...